A Real-Time Traffic Surveillance and Security System using Transfer Learning and Edge Computing

2020 ◽  
Author(s):  
Aaron Joseph Fernandez ◽  
Ajay K S ◽  
Antony Jose ◽  
Austin Kuruvila M ◽  
Varun G Menon ◽  
...  
Keyword(s):  
Real Time ◽  
Transfer Learning ◽  
Security System ◽  
Edge Computing ◽  
Traffic Surveillance ◽  
Real Time Traffic

Real-Time Traffic Measurement from Single Loop Inductive Signatures

2002 ◽  
Vol 1804 (1) ◽  
pp. 98-106 ◽  
Author(s):  
Seri Oh ◽  
Stephen G. Ritchie ◽  
Cheol Oh
Keyword(s):  
Real Time ◽  
The United States ◽  
Traffic Data ◽  
Traffic Surveillance ◽  
Vehicle Class ◽  
Traffic Measurement ◽  
Single Loop ◽  
Loop Detectors ◽  
Real Time Traffic ◽  
Traffic Data Collection

Accurate traffic data acquisition is essential for effective traffic surveillance, which is the backbone of advanced transportation management and information systems (ATMIS). Inductive loop detectors (ILDs) are still widely used for traffic data collection in the United States and many other countries. Three fundamental traffic parameters—speed, volume, and occupancy—are obtainable via single or double (speed-trap) ILDs. Real-time knowledge of such traffic parameters typically is required for use in ATMIS from a single loop detector station, which is the most commonly used. However, vehicle speeds cannot be obtained directly. Hence, the ability to estimate vehicle speeds accurately from single loop detectors is of considerable interest. In addition, operating agencies report that conventional loop detectors are unable to achieve volume count accuracies of more than 90% to 95%. The improved derivation of fundamental real-time traffic parameters, such as speed, volume, occupancy, and vehicle class, from single loop detectors and inductive signatures is demonstrated.

Large-scale machine learning and evaluation platform for real-time traffic surveillance

2016 ◽  
Vol 25 (5) ◽  
pp. 051204
Author(s):  
Justin A. Eichel ◽  
Akshaya Mishra ◽  
Nicholas Miller ◽  
Nicholas Jankovic ◽  
Mohan A. Thomas ◽  
...  
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Large Scale ◽  
Traffic Surveillance ◽  
Real Time Traffic ◽  
Evaluation Platform

Real-time traffic surveillance using ZigBee

2010 ◽  
Author(s):  
Aisha Al-Abdallah ◽  
Asma Al-Emadi ◽  
Mona Al-Ansari ◽  
Nassma Mohandes ◽  
Qutaibah Malluhi
Keyword(s):  
Real Time ◽  
Traffic Surveillance ◽  
Real Time Traffic

Anonymous Vehicle Tracking for Real-Time Traffic Surveillance and Performance on Signalized Arterials

2003 ◽  
Vol 1826 (1) ◽  
pp. 37-44 ◽  
Author(s):  
Cheol Oh ◽  
Stephen G. Ritchie
Keyword(s):  
Real Time ◽  
Traffic Control ◽  
Search Space ◽  
Vehicle Tracking ◽  
Research Experience ◽  
Traffic Surveillance ◽  
Real Time Traffic ◽  
Space Reduction ◽  
Vehicle Reidentification ◽  
Signalized Arterials

One of the fundamental requirements for facilitating implementation of any advanced transportation management and information system (ATMIS) is the development of a real-time traffic surveillance system able to produce reliable and accurate traffic performance measures. This study presents a new framework for anonymous vehicle tracking capable of tracing individual vehicles by the vehicle features. The core part of the proposed vehicle tracking method is a vehicle reidentification algorithm for signalized intersections based on inductive vehicle signatures. The new vehicle reidentification system consists of two major components: search space reduction and probabilistic pattern recognition. Not only real-time intersection performance but also intersection origin–destination information can be obtained as the algorithm’s basic output. A systematic simulation investigation was conducted of the performance and feasibility of anonymous vehicle tracking on signalized arterials using the Paramics simulation model. Extensive research experience with vehicle reidentification techniques on single roadway segments was the basis for investigating the performance that could be obtained from tracking individual vehicles across multiple detector stations. The findings of this study serve as a logical and necessary precursor to possible field implementation of vehicle reidentification techniques. The proposed anonymous vehicle tracking methodology with existing traffic surveillance infrastructure would be an invaluable tool for operating agencies in support of ATMIS strategies for congestion monitoring, adaptive traffic control, system evaluation, and provision of real-time traveler information.

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